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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark
import scala.collection.JavaConversions._
import scala.collection.mutable
import scala.concurrent.Await
import scala.util.Properties
import akka.actor._
import com.google.common.collect.MapMaker
import org.apache.spark.api.python.PythonWorkerFactory
import org.apache.spark.broadcast.BroadcastManager
import org.apache.spark.metrics.MetricsSystem
import org.apache.spark.network.ConnectionManager
import org.apache.spark.scheduler.LiveListenerBus
import org.apache.spark.serializer.Serializer
import org.apache.spark.storage._
import org.apache.spark.util.{AkkaUtils, Utils}
/**
* Holds all the runtime environment objects for a running Spark instance (either master or worker),
* including the serializer, Akka actor system, block manager, map output tracker, etc. Currently
* Spark code finds the SparkEnv through a thread-local variable, so each thread that accesses these
* objects needs to have the right SparkEnv set. You can get the current environment with
* SparkEnv.get (e.g. after creating a SparkContext) and set it with SparkEnv.set.
*/
class SparkEnv private[spark] (
val executorId: String,
val actorSystem: ActorSystem,
val serializer: Serializer,
val closureSerializer: Serializer,
val cacheManager: CacheManager,
val mapOutputTracker: MapOutputTracker,
val shuffleFetcher: ShuffleFetcher,
val broadcastManager: BroadcastManager,
val blockManager: BlockManager,
val connectionManager: ConnectionManager,
val securityManager: SecurityManager,
val httpFileServer: HttpFileServer,
val sparkFilesDir: String,
val metricsSystem: MetricsSystem,
val conf: SparkConf) extends Logging {
// A mapping of thread ID to amount of memory used for shuffle in bytes
// All accesses should be manually synchronized
val shuffleMemoryMap = mutable.HashMap[Long, Long]()
private val pythonWorkers = mutable.HashMap[(String, Map[String, String]), PythonWorkerFactory]()
// A general, soft-reference map for metadata needed during HadoopRDD split computation
// (e.g., HadoopFileRDD uses this to cache JobConfs and InputFormats).
private[spark] val hadoopJobMetadata = new MapMaker().softValues().makeMap[String, Any]()
private[spark] def stop() {
pythonWorkers.foreach { case(key, worker) => worker.stop() }
httpFileServer.stop()
mapOutputTracker.stop()
shuffleFetcher.stop()
broadcastManager.stop()
blockManager.stop()
blockManager.master.stop()
metricsSystem.stop()
actorSystem.shutdown()
// Unfortunately Akka's awaitTermination doesn't actually wait for the Netty server to shut
// down, but let's call it anyway in case it gets fixed in a later release
// UPDATE: In Akka 2.1.x, this hangs if there are remote actors, so we can't call it.
// actorSystem.awaitTermination()
}
private[spark]
def createPythonWorker(pythonExec: String, envVars: Map[String, String]): java.net.Socket = {
synchronized {
val key = (pythonExec, envVars)
pythonWorkers.getOrElseUpdate(key, new PythonWorkerFactory(pythonExec, envVars)).create()
}
}
}
object SparkEnv extends Logging {
private val env = new ThreadLocal[SparkEnv]
@volatile private var lastSetSparkEnv : SparkEnv = _
def set(e: SparkEnv) {
lastSetSparkEnv = e
env.set(e)
}
/**
* Returns the ThreadLocal SparkEnv, if non-null. Else returns the SparkEnv
* previously set in any thread.
*/
def get: SparkEnv = {
Option(env.get()).getOrElse(lastSetSparkEnv)
}
/**
* Returns the ThreadLocal SparkEnv.
*/
def getThreadLocal: SparkEnv = {
env.get()
}
private[spark] def create(
conf: SparkConf,
executorId: String,
hostname: String,
port: Int,
isDriver: Boolean,
isLocal: Boolean,
listenerBus: LiveListenerBus = null): SparkEnv = {
// Listener bus is only used on the driver
if (isDriver) {
assert(listenerBus != null, "Attempted to create driver SparkEnv with null listener bus!")
}
val securityManager = new SecurityManager(conf)
val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, port, conf = conf,
securityManager = securityManager)
// Bit of a hack: If this is the driver and our port was 0 (meaning bind to any free port),
// figure out which port number Akka actually bound to and set spark.driver.port to it.
if (isDriver && port == 0) {
conf.set("spark.driver.port", boundPort.toString)
}
val classLoader = Thread.currentThread.getContextClassLoader
// Create an instance of the class named by the given Java system property, or by
// defaultClassName if the property is not set, and return it as a T
def instantiateClass[T](propertyName: String, defaultClassName: String): T = {
val name = conf.get(propertyName, defaultClassName)
val cls = Class.forName(name, true, classLoader)
// First try with the constructor that takes SparkConf. If we can't find one,
// use a no-arg constructor instead.
try {
cls.getConstructor(classOf[SparkConf]).newInstance(conf).asInstanceOf[T]
} catch {
case _: NoSuchMethodException =>
cls.getConstructor().newInstance().asInstanceOf[T]
}
}
val serializer = instantiateClass[Serializer](
"spark.serializer", "org.apache.spark.serializer.JavaSerializer")
val closureSerializer = instantiateClass[Serializer](
"spark.closure.serializer", "org.apache.spark.serializer.JavaSerializer")
def registerOrLookup(name: String, newActor: => Actor): ActorRef = {
if (isDriver) {
logInfo("Registering " + name)
actorSystem.actorOf(Props(newActor), name = name)
} else {
val driverHost: String = conf.get("spark.driver.host", "localhost")
val driverPort: Int = conf.getInt("spark.driver.port", 7077)
Utils.checkHost(driverHost, "Expected hostname")
val url = s"akka.tcp://spark@$driverHost:$driverPort/user/$name"
val timeout = AkkaUtils.lookupTimeout(conf)
logInfo(s"Connecting to $name: $url")
Await.result(actorSystem.actorSelection(url).resolveOne(timeout), timeout)
}
}
val blockManagerMaster = new BlockManagerMaster(registerOrLookup(
"BlockManagerMaster",
new BlockManagerMasterActor(isLocal, conf, listenerBus)), conf)
val blockManager = new BlockManager(executorId, actorSystem, blockManagerMaster,
serializer, conf, securityManager)
val connectionManager = blockManager.connectionManager
val broadcastManager = new BroadcastManager(isDriver, conf, securityManager)
val cacheManager = new CacheManager(blockManager)
// Have to assign trackerActor after initialization as MapOutputTrackerActor
// requires the MapOutputTracker itself
val mapOutputTracker = if (isDriver) {
new MapOutputTrackerMaster(conf)
} else {
new MapOutputTracker(conf)
}
mapOutputTracker.trackerActor = registerOrLookup(
"MapOutputTracker",
new MapOutputTrackerMasterActor(mapOutputTracker.asInstanceOf[MapOutputTrackerMaster], conf))
val shuffleFetcher = instantiateClass[ShuffleFetcher](
"spark.shuffle.fetcher", "org.apache.spark.BlockStoreShuffleFetcher")
val httpFileServer = new HttpFileServer(securityManager)
httpFileServer.initialize()
conf.set("spark.fileserver.uri", httpFileServer.serverUri)
val metricsSystem = if (isDriver) {
MetricsSystem.createMetricsSystem("driver", conf, securityManager)
} else {
MetricsSystem.createMetricsSystem("executor", conf, securityManager)
}
metricsSystem.start()
// Set the sparkFiles directory, used when downloading dependencies. In local mode,
// this is a temporary directory; in distributed mode, this is the executor's current working
// directory.
val sparkFilesDir: String = if (isDriver) {
Utils.createTempDir().getAbsolutePath
} else {
"."
}
// Warn about deprecated spark.cache.class property
if (conf.contains("spark.cache.class")) {
logWarning("The spark.cache.class property is no longer being used! Specify storage " +
"levels using the RDD.persist() method instead.")
}
new SparkEnv(
executorId,
actorSystem,
serializer,
closureSerializer,
cacheManager,
mapOutputTracker,
shuffleFetcher,
broadcastManager,
blockManager,
connectionManager,
securityManager,
httpFileServer,
sparkFilesDir,
metricsSystem,
conf)
}
/**
* Return a map representation of jvm information, Spark properties, system properties, and
* class paths. Map keys define the category, and map values represent the corresponding
* attributes as a sequence of KV pairs. This is used mainly for SparkListenerEnvironmentUpdate.
*/
private[spark]
def environmentDetails(
conf: SparkConf,
schedulingMode: String,
addedJars: Seq[String],
addedFiles: Seq[String]): Map[String, Seq[(String, String)]] = {
val jvmInformation = Seq(
("Java Version", "%s (%s)".format(Properties.javaVersion, Properties.javaVendor)),
("Java Home", Properties.javaHome),
("Scala Version", Properties.versionString),
("Scala Home", Properties.scalaHome)
).sorted
// Spark properties
// This includes the scheduling mode whether or not it is configured (used by SparkUI)
val schedulerMode =
if (!conf.contains("spark.scheduler.mode")) {
Seq(("spark.scheduler.mode", schedulingMode))
} else {
Seq[(String, String)]()
}
val sparkProperties = (conf.getAll ++ schedulerMode).sorted
// System properties that are not java classpaths
val systemProperties = System.getProperties.iterator.toSeq
val otherProperties = systemProperties.filter { case (k, v) =>
k != "java.class.path" && !k.startsWith("spark.")
}.sorted
// Class paths including all added jars and files
val classPathProperty = systemProperties.find { case (k, v) =>
k == "java.class.path"
}.getOrElse(("", ""))
val classPathEntries = classPathProperty._2
.split(conf.get("path.separator", ":"))
.filterNot(e => e.isEmpty)
.map(e => (e, "System Classpath"))
val addedJarsAndFiles = (addedJars ++ addedFiles).map((_, "Added By User"))
val classPaths = (addedJarsAndFiles ++ classPathEntries).sorted
Map[String, Seq[(String, String)]](
"JVM Information" -> jvmInformation,
"Spark Properties" -> sparkProperties,
"System Properties" -> otherProperties,
"Classpath Entries" -> classPaths)
}
}
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